SaniPath Data Analysis/ Data Discovery

Overview

Descriptive Statistics by City

Deployments Overview:

country	city	year	citylabel
India	Vellore	2014	Vellore 14
Mozambique	Maputo	2015	Maputo 15
Ghana	Accra	2016	Accra 16
Mozambique	Maputo	2016	Maputo 16
Cambodia	Siem Reap	2016	Siem Reap 16
USA	Atlanta	2016	Atlanta 16
Bangladesh	Dhaka	2017	Dhaka 17
Zambia	Lusaka	2018	Lusaka 18
Ghana	Accra	2018	Accra 18
Ghana	Kumasi	2018	Kumasi 18
Uganda	Kampala	2018	Kampala 18
Zambia	Lusaka	2019	Lusaka 19
Senegal	Dakar	2019	Dakar 19

In this database there are 13 SaniPath deployments.

Number of Neighborhoods

Neighborhoods Overview:

country	city	year	neighborhood_id	neighb_UID	neighborhood	note
India	Vellore	2014	1	1101	Old Town
India	Vellore	2014	2	1102	Chinna Allapuram
Mozambique	Maputo	2015	1	501	Intervention
Mozambique	Maputo	2015	2	502	Control
Ghana	Accra	2016	1	301	Shiabu
Ghana	Accra	2016	2	302	Chorkor
Ghana	Accra	2016	3	303	Kokomlemle
Ghana	Accra	2016	4	304	Ringway
Ghana	Accra	2016	5	305	Adabraka
Mozambique	Maputo	2016	1	801	Intervention
Mozambique	Maputo	2016	2	802	Control
Cambodia	Siem Reap	2016	1	101	Chong Kaosou	Informal
Cambodia	Siem Reap	2016	2	102	Kumruthemey (informal)	Informal
Cambodia	Siem Reap	2016	3	103	Kumruthemey (formal)	Formal
Cambodia	Siem Reap	2016	4	104	Steung Thumey	Formal
Cambodia	Siem Reap	2016	5	105	Veal/ Trapangses	Formal
USA	Atlanta	2016	1	1001	Peoplestown
Bangladesh	Dhaka	2017	1	201	Kalshi	North
Bangladesh	Dhaka	2017	2	202	Badda	North
Bangladesh	Dhaka	2017	3	203	Gabtoli	North
Bangladesh	Dhaka	2017	4	204	Uttarkhan	North
Bangladesh	Dhaka	2017	5	205	Gulshan	North
Bangladesh	Dhaka	2017	6	206	Kamalapur	South
Bangladesh	Dhaka	2017	7	207	Shampur	South
Bangladesh	Dhaka	2017	8	208	Hazaribagh	South
Bangladesh	Dhaka	2017	9	209	Motijhil	South
Bangladesh	Dhaka	2017	10	210	Dhanmondi	South
Zambia	Lusaka	2018	1	401	Kanyama
Ghana	Accra	2018	1	601	Mataheko
Ghana	Accra	2018	2	602	Osu Alata
Ghana	Kumasi	2018	1	701	FanteNewTown
Ghana	Kumasi	2018	2	702	MoshieZongo
Ghana	Kumasi	2018	3	703	Dakodwom
Ghana	Kumasi	2018	4	704	Ahodwo
Uganda	Kampala	2018	1	901	Makindye
Uganda	Kampala	2018	2	902	Central
Uganda	Kampala	2018	3	903	Kawempe
Uganda	Kampala	2018	4	904	Rubaga
Uganda	Kampala	2018	5	905	Nakawa
Zambia	Lusaka	2019	1	1301	Chawama
Zambia	Lusaka	2019	2	1302	Chazanga
Zambia	Lusaka	2019	3	1303	George
Senegal	Dakar	2019	1	1201	WakhinaneNimzatt
Senegal	Dakar	2019	2	1202	MedinaGounass
Senegal	Dakar	2019	3	1203	DTK
Senegal	Dakar	2019	4	1204	RufisqueEst
Senegal	Dakar	2019	5	1205	SicapLiberte

Overall, there are 13 deployments in 9 different countries, 10 cities, and 47 neighborhoods in this analysis.

Number of Samples per Pathway

Pathway Overview:

	Vellore 14	Maputo 15	Accra 16	Maputo 16	Siem Reap 16	Atlanta 16	Dhaka 17	Lusaka 18	Accra 18	Kumasi 18	Kampala 18	Lusaka 19	Dakar 19
Open Drain Water		15	163	7			100	20	21	40	47	30	50
Raw Produce	20		70	23	33	10	100	20	20	39	50	30	50
Municipal Drinking Water	22		68	40	10	10	100	10	20	36	39	30	100
Ocean			30						10
Surface Water							100			3	12	10
Floodwater		10	9	10	50	7	100	30	9	36	50
Public Latrine	24	28	260	43		10	100	20	20	27	50	30
Soil	20	74	88	76	50	10	100	20	20	40	50	30	50
Bathing Water	20	25		25	10		100		9	21
Street Food							100	20	20	40	45	30	50
Other Drinking Water					150		100	30			39	60

Overall, 4053 samples were collected and analyized.

Survey Type and Number of Surveys per Deployment

Survey Type Overview:

Form	Vellore 14	Maputo 15	Accra 16	Maputo 16	Siem Reap 16	Atlanta 16	Dhaka 17	Lusaka 18	Accra 18	Kumasi 18	Kampala 18	Lusaka 19	Dakar 19
Behavioral Survey
Household	200	125	821	136	410	23	823	100	200	400	548	300	500
Community	8		22				28	4	8	16	10	12	20
School	8		12				35	4	8	16	9	12	20
Environmental Testing
Sample	106	152	688	224	303	47	1000	170	149	282	382	250	300
Lab	106	152	688	224	303	47	1000	170	149	282	382	250	300

In total, there were 4586 Household Surveys, 128 Community Surveys and 124 School Surveys conducted. Additionally, 4053 environmental samples were collected and subsequently analyzed in the laboratory.

Survey Type and Number of People per Deployment

Form Participants:

Form	Vellore 14	Maputo 15	Accra 16	Maputo 16	Siem Reap 16	Atlanta 16	Dhaka 17	Lusaka 18	Accra 18	Kumasi 18	Kampala 18	Lusaka 19	Dakar 19
Household	200	125	821	136	410	23	823	100	200	400	548	300	500
Community	117		293				501	79	127	240	112	219	300
School	151		315				597	73	120	320	114	240	300

Note: Household survey numbers include the number of surveys, whereas community and school surveys account for number of participants.

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Literature

Population Density

• Cambodia, Siem Reap:
• Bangladesh, Dhaka:
  Census 2011, Report: Female/ Male Population (Table C02), Population Density (Table C01)
• Ghana, Accra:
  Census 2010, Report:
  Greater Accra Region: 1235.8 population density in 2010, 895.5 population density in 2000 (people per kmsq) (Table 4.4).
  90% of the population is considered urban population (Table 4.5).
  
• Zambia, Lusaka:
  Census 2010, Report: Population density for Lusaka 63.5 (2000) and 100.1 (2010) people per kmsq (Table 2.7).
  Census 2010, Population.
• Mozambique, Maputo:
  Note: added population information to meta_neighborhood.csv file. Sources: Ghana Statistical Services - Census 2010 Shapefile from Habib.

Enteric Disease Burden

Fecal Sludge Managment

Shit Flow Diagram

• Cambodia, Siem Reap:
  no SFD available
• Bangladesh, Dhaka:
  SFD Link. The SFD report from March 2016 indicates that less than 1% of fecal sludge in Dhaka is safely managed.
• Ghana, Accra:
  no SFD available
• Zambia, Lusaka:
  SFD Link. The SFD report from September 2018 shows that 17% of fecal sludge is safely managed.
• Mozambique, Maputo:
  no SFD available
• Ghana, Kumasi:
  SFD Link. The SFD report from November 2015 shows that 55% of fecal sludge is safely managed.

WASH Interventions in the past 10 Years

Local Stakeholders/ Decision Makers

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MF vs. IDEXX

How many samples were collected for each deployment and each pathway by laboratory analysis method: Membrane Filtration (MF) or IDEXX Quanti-Tray (IDEXX).

Lab Method used:

	IDEXX	MF
Vellore 14	0	106
Maputo 15	0	152
Accra 16	0	688
Maputo 16	0	224
Siem Reap 16	0	303
Atlanta 16	47	0
Dhaka 17	1000	0
Lusaka 18	170	0
Accra 18	0	149
Kumasi 18	0	282
Kampala 18	0	382
Lusaka 19	250	0
Dakar 19	0	300

Lab Method per Sample Type:

	IDEXX	MF
Open Drain Water	150	343
Raw Produce	160	305
Municipal Drinking Water	150	335
Ocean	0	40
Surface Water	110	15
Floodwater	137	174
Public Latrine	160	452
Soil	160	468
Bathing Water	100	110
Street Food	150	155
Other Drinking Water	190	189

Membrane Filtration: valid vs. invalid test results

How many samples tested with the Membrane Filtration method produced valid or invalid results?
Note: Invalid MF results include TDTC (Too Dirty To Count) classifications of laboratory results of samples. Valid MF results are all results between the range of 0 and 200 as well as TNTC (Too Numerous To Count).

Overview: Valid Membrane Filtration Lab Results

n	valid	invalid	percent valid
6030	5846	184	96.95

Valid Membrane Filtration Lab Results by Deployment

citylabel	n	valid	invalid	percent valid
Vellore 14	212	212	0	100
Maputo 15	353	337	16	95.47
Accra 16	1539	1509	30	98.05
Maputo 16	564	503	61	89.18
Siem Reap 16	739	725	14	98.11
Accra 18	366	365	1	99.73
Kumasi 18	708	670	38	94.63
Kampala 18	947	924	23	97.57
Dakar 19	602	601	1	99.83

Valid Membrane Filtration Lab Results by Sample Type

sample type	n	valid	invalid	percent valid
Open Drain Water	877	872	5	99.43
Raw Produce	770	742	28	96.36
Municipal Drinking Water	671	671	0	100
Ocean	80	80	0	100
Surface Water	32	31	1	96.88
Floodwater	495	485	10	97.98
Public Latrine	910	890	20	97.8
Soil	1185	1073	112	90.55
Bathing Water	220	219	1	99.55
Street Food	410	408	2	99.51
Other Drinking Water	380	375	5	98.68

Valid Membrane Filtration Lab Results by Deployment and Sample Type

citylabel	sample type	n	valid	invalid	percent valid
Vellore 14	Raw Produce	40	40	0	100
Vellore 14	Municipal Drinking Water	44	44	0	100
Vellore 14	Public Latrine	48	48	0	100
Vellore 14	Soil	40	40	0	100
Vellore 14	Bathing Water	40	40	0	100
Maputo 15	Open Drain Water	45	45	0	100
Maputo 15	Floodwater	30	27	3	90
Maputo 15	Public Latrine	56	56	0	100
Maputo 15	Soil	172	159	13	92.44
Maputo 15	Bathing Water	50	50	0	100
Accra 16	Open Drain Water	489	489	0	100
Accra 16	Raw Produce	140	135	5	96.43
Accra 16	Municipal Drinking Water	136	136	0	100
Accra 16	Ocean	60	60	0	100
Accra 16	Floodwater	18	18	0	100
Accra 16	Public Latrine	520	505	15	97.12
Accra 16	Soil	176	166	10	94.32
Maputo 16	Open Drain Water	21	21	0	100
Maputo 16	Raw Produce	69	67	2	97.1
Maputo 16	Municipal Drinking Water	80	80	0	100
Maputo 16	Floodwater	30	27	3	90
Maputo 16	Public Latrine	86	86	0	100
Maputo 16	Soil	228	172	56	75.44
Maputo 16	Bathing Water	50	50	0	100
Siem Reap 16	Raw Produce	99	94	5	94.95
Siem Reap 16	Municipal Drinking Water	20	20	0	100
Siem Reap 16	Floodwater	150	149	1	99.33
Siem Reap 16	Soil	150	147	3	98
Siem Reap 16	Bathing Water	20	20	0	100
Siem Reap 16	Other Drinking Water	300	295	5	98.33
Accra 18	Open Drain Water	42	42	0	100
Accra 18	Raw Produce	59	59	0	100
Accra 18	Municipal Drinking Water	40	40	0	100
Accra 18	Ocean	20	20	0	100
Accra 18	Floodwater	27	27	0	100
Accra 18	Public Latrine	40	40	0	100
Accra 18	Soil	60	59	1	98.33
Accra 18	Bathing Water	18	18	0	100
Accra 18	Street Food	60	60	0	100
Kumasi 18	Open Drain Water	80	79	1	98.75
Kumasi 18	Raw Produce	117	105	12	89.74
Kumasi 18	Municipal Drinking Water	72	72	0	100
Kumasi 18	Surface Water	6	6	0	100
Kumasi 18	Floodwater	97	94	3	96.91
Kumasi 18	Public Latrine	54	53	1	98.15
Kumasi 18	Soil	120	101	19	84.17
Kumasi 18	Bathing Water	42	41	1	97.62
Kumasi 18	Street Food	120	119	1	99.17
Kampala 18	Open Drain Water	100	97	3	97
Kampala 18	Raw Produce	146	142	4	97.26
Kampala 18	Municipal Drinking Water	78	78	0	100
Kampala 18	Surface Water	26	25	1	96.15
Kampala 18	Floodwater	143	143	0	100
Kampala 18	Public Latrine	106	102	4	96.23
Kampala 18	Soil	139	129	10	92.81
Kampala 18	Street Food	129	128	1	99.22
Kampala 18	Other Drinking Water	80	80	0	100
Dakar 19	Open Drain Water	100	99	1	99
Dakar 19	Raw Produce	100	100	0	100
Dakar 19	Municipal Drinking Water	201	201	0	100
Dakar 19	Soil	100	100	0	100
Dakar 19	Street Food	101	101	0	100

These results suggest sample types with a low percentage of validity of MF testing could improve the overall data quality by addressing potential sampling problems in training. Numbers in the “Percent Valid” column in RED indicate a rate below 80%.

IDEXX: valid vs. invalid test results

How many samples tested with IDEXX method produced a valid or invalid result?

Valid IDEXX Lab Results

n	valid	invalid	percent valid
3668	3667	1	99.97

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Dilution Error

When a dilution error occurs, meaning that a sample was not properly processed in the laboratory, the SaniPath Tool automatically discards the E. coli results of this sample (e.g. when the E. coli count for the second dilution is higher than the first dilution - including a variety of possible combinations of illogical results and potential errors).
This section explores how many samples were lost due to dilution error.

Dilution Error Overview:

Dilution Error by City

citylabel	omitted	n	percentage
Vellore 14	9	106	8.49
Maputo 15	12	152	7.89
Accra 16	18	688	2.62
Maputo 16	10	224	4.46
Siem Reap 16	5	303	1.65
Atlanta 16	0	47	0.00
Dhaka 17	53	1000	5.30
Lusaka 18	18	170	10.59
Accra 18	0	149	0.00
Kumasi 18	3	282	1.06
Kampala 18	46	382	12.04
Lusaka 19	2	250	0.80
Dakar 19	15	300	5.00

Dilution Error By city, by neighborhood

Dilution Error by City and Neighborhood

citylabel	neighb_UID	omitted	n	percentage
Vellore 14	1101	2	53	3.77
Vellore 14	1102	7	53	13.21
Maputo 15	501	7	98	7.14
Maputo 15	502	5	54	9.26
Accra 16	301	3	93	3.23
Accra 16	302	4	253	1.58
Accra 16	303	7	124	5.65
Accra 16	304	2	85	2.35
Accra 16	305	2	133	1.50
Maputo 16	801	8	152	5.26
Maputo 16	802	2	72	2.78
Siem Reap 16	101	2	70	2.86
Siem Reap 16	102	2	60	3.33
Siem Reap 16	103	1	49	2.04
Dhaka 17	201	8	100	8.00
Dhaka 17	202	2	100	2.00
Dhaka 17	203	3	100	3.00
Dhaka 17	204	9	100	9.00
Dhaka 17	205	2	100	2.00
Dhaka 17	206	6	100	6.00
Dhaka 17	207	3	100	3.00
Dhaka 17	208	6	100	6.00
Dhaka 17	209	10	100	10.00
Dhaka 17	210	4	100	4.00
Lusaka 18	401	18	170	10.59
Kumasi 18	701	2	68	2.94
Kumasi 18	702	1	79	1.27
Kampala 18	901	14	67	20.90
Kampala 18	902	9	78	11.54
Kampala 18	903	2	78	2.56
Kampala 18	904	15	77	19.48
Kampala 18	905	6	82	7.32
Lusaka 19	1301	2	90	2.22
Dakar 19	1201	3	60	5.00
Dakar 19	1202	1	60	1.67
Dakar 19	1203	2	60	3.33
Dakar 19	1204	8	60	13.33
Dakar 19	1205	1	60	1.67

Dilution Error By city, by neighborhood, by sample

Dilution Error by City and Neighborhood and Sample Type

citylabel	neighb_UID	sample_type_name	omitted	n	percentage
Vellore 14	1101	Municipal Drinking Water	2	11	18.18
Vellore 14	1102	Municipal Drinking Water	2	11	18.18
Vellore 14	1102	Public Latrine	1	12	8.33
Vellore 14	1102	Soil	1	10	10.00
Vellore 14	1102	Bathing Water	3	10	30.00
Maputo 15	501	Open Drain Water	1	9	11.11
Maputo 15	501	Soil	5	54	9.26
Maputo 15	501	Bathing Water	1	15	6.67
Maputo 15	502	Open Drain Water	1	6	16.67
Maputo 15	502	Floodwater	2	5	40.00
Maputo 15	502	Soil	2	20	10.00
Accra 16	301	Open Drain Water	1	16	6.25
Accra 16	301	Raw Produce	1	20	5.00
Accra 16	301	Soil	1	18	5.56
Accra 16	302	Raw Produce	2	20	10.00
Accra 16	302	Public Latrine	1	95	1.05
Accra 16	302	Soil	1	40	2.50
Accra 16	303	Raw Produce	5	9	55.56
Accra 16	303	Public Latrine	2	59	3.39
Accra 16	304	Raw Produce	1	10	10.00
Accra 16	304	Public Latrine	1	29	3.45
Accra 16	305	Raw Produce	2	11	18.18
Maputo 16	801	Municipal Drinking Water	1	30	3.33
Maputo 16	801	Public Latrine	5	30	16.67
Maputo 16	801	Soil	1	57	1.75
Maputo 16	801	Bathing Water	1	15	6.67
Maputo 16	802	Raw Produce	1	8	12.50
Maputo 16	802	Municipal Drinking Water	1	10	10.00
Siem Reap 16	101	Raw Produce	2	10	20.00
Siem Reap 16	102	Raw Produce	2	10	20.00
Siem Reap 16	103	Other Drinking Water	1	29	3.45
Dhaka 17	201	Open Drain Water	1	10	10.00
Dhaka 17	201	Raw Produce	1	10	10.00
Dhaka 17	201	Floodwater	2	10	20.00
Dhaka 17	201	Soil	2	10	20.00
Dhaka 17	201	Street Food	2	10	20.00
Dhaka 17	202	Raw Produce	2	10	20.00
Dhaka 17	203	Raw Produce	1	10	10.00
Dhaka 17	203	Soil	1	10	10.00
Dhaka 17	203	Street Food	1	10	10.00
Dhaka 17	204	Raw Produce	2	10	20.00
Dhaka 17	204	Surface Water	1	10	10.00
Dhaka 17	204	Floodwater	2	10	20.00
Dhaka 17	204	Soil	1	10	10.00
Dhaka 17	204	Street Food	3	10	30.00
Dhaka 17	205	Floodwater	1	10	10.00
Dhaka 17	205	Street Food	1	10	10.00
Dhaka 17	206	Raw Produce	2	10	20.00
Dhaka 17	206	Surface Water	1	10	10.00
Dhaka 17	206	Floodwater	1	10	10.00
Dhaka 17	206	Soil	1	10	10.00
Dhaka 17	206	Street Food	1	10	10.00
Dhaka 17	207	Raw Produce	2	10	20.00
Dhaka 17	207	Soil	1	10	10.00
Dhaka 17	208	Raw Produce	1	10	10.00
Dhaka 17	208	Floodwater	1	10	10.00
Dhaka 17	208	Soil	2	10	20.00
Dhaka 17	208	Street Food	2	10	20.00
Dhaka 17	209	Raw Produce	2	10	20.00
Dhaka 17	209	Surface Water	2	10	20.00
Dhaka 17	209	Soil	2	10	20.00
Dhaka 17	209	Street Food	4	10	40.00
Dhaka 17	210	Surface Water	1	10	10.00
Dhaka 17	210	Soil	1	10	10.00
Dhaka 17	210	Street Food	2	10	20.00
Lusaka 18	401	Open Drain Water	2	20	10.00
Lusaka 18	401	Raw Produce	1	20	5.00
Lusaka 18	401	Floodwater	10	30	33.33
Lusaka 18	401	Soil	4	20	20.00
Lusaka 18	401	Street Food	1	20	5.00
Kumasi 18	701	Raw Produce	1	9	11.11
Kumasi 18	701	Street Food	1	10	10.00
Kumasi 18	702	Public Latrine	1	9	11.11
Kampala 18	901	Raw Produce	1	10	10.00
Kampala 18	901	Surface Water	3	8	37.50
Kampala 18	901	Public Latrine	3	10	30.00
Kampala 18	901	Soil	2	10	20.00
Kampala 18	901	Street Food	5	10	50.00
Kampala 18	902	Raw Produce	4	10	40.00
Kampala 18	902	Soil	1	10	10.00
Kampala 18	902	Street Food	3	9	33.33
Kampala 18	902	Other Drinking Water	1	10	10.00
Kampala 18	903	Raw Produce	1	10	10.00
Kampala 18	903	Soil	1	10	10.00
Kampala 18	904	Raw Produce	3	10	30.00
Kampala 18	904	Floodwater	3	10	30.00
Kampala 18	904	Soil	4	10	40.00
Kampala 18	904	Street Food	4	10	40.00
Kampala 18	904	Other Drinking Water	1	10	10.00
Kampala 18	905	Floodwater	1	10	10.00
Kampala 18	905	Soil	2	10	20.00
Kampala 18	905	Other Drinking Water	3	10	30.00
Lusaka 19	1301	Soil	2	10	20.00
Dakar 19	1201	Soil	2	10	20.00
Dakar 19	1201	Street Food	1	10	10.00
Dakar 19	1202	Open Drain Water	1	10	10.00
Dakar 19	1203	Open Drain Water	1	10	10.00
Dakar 19	1203	Soil	1	10	10.00
Dakar 19	1204	Open Drain Water	7	10	70.00
Dakar 19	1204	Soil	1	10	10.00
Dakar 19	1205	Soil	1	10	10.00

Sample Size Minimum for Model:

To correctly perform the QMRA calculations, a minimum sample size of 10 samples has to be met. Which sample types per neighborhood and deployment have fulfilled the requirement? (omitted = sample discarded because of error)

Number of pathways that did not meet the requred 10 sample minimum, per city:

Requirements Not Met - Overview

citylabel	neighb_UID	not_met
Maputo 15	501	2
Maputo 15	502	2
Accra 16	303	2
Accra 16	304	1
Accra 16	305	1
Maputo 16	801	1
Maputo 16	802	3
Siem Reap 16	105	1
Atlanta 16	1001	1
Accra 18	601	2
Accra 18	602	2
Kumasi 18	701	3
Kumasi 18	702	1
Kumasi 18	703	3
Kumasi 18	704	2
Kampala 18	901	2
Kampala 18	902	2
Kampala 18	903	4
Kampala 18	904	1

Minimum Requirement Met?

citylabel	neighb_UID	sample_type_name	omitted	n	requ_met	net_n	net_requ_met
Vellore 14	1101	Raw Produce	0	10	Yes	10	Yes
Vellore 14	1101	Municipal Drinking Water	2	11	Yes	9	No
Vellore 14	1101	Public Latrine	0	12	Yes	12	Yes
Vellore 14	1101	Soil	0	10	Yes	10	Yes
Vellore 14	1101	Bathing Water	0	10	Yes	10	Yes
Vellore 14	1102	Raw Produce	0	10	Yes	10	Yes
Vellore 14	1102	Municipal Drinking Water	2	11	Yes	9	No
Vellore 14	1102	Public Latrine	1	12	Yes	11	Yes
Vellore 14	1102	Soil	1	10	Yes	9	No
Vellore 14	1102	Bathing Water	3	10	Yes	7	No
Maputo 15	501	Open Drain Water	1	9	No	8	No
Maputo 15	501	Floodwater	0	5	No	5	No
Maputo 15	501	Public Latrine	0	15	Yes	15	Yes
Maputo 15	501	Soil	5	54	Yes	49	Yes
Maputo 15	501	Bathing Water	1	15	Yes	14	Yes
Maputo 15	502	Open Drain Water	1	6	No	5	No
Maputo 15	502	Floodwater	2	5	No	3	No
Maputo 15	502	Public Latrine	0	13	Yes	13	Yes
Maputo 15	502	Soil	2	20	Yes	18	Yes
Maputo 15	502	Bathing Water	0	10	Yes	10	Yes
Accra 16	301	Open Drain Water	1	16	Yes	15	Yes
Accra 16	301	Raw Produce	1	20	Yes	19	Yes
Accra 16	301	Municipal Drinking Water	0	10	Yes	10	Yes
Accra 16	301	Ocean	0	10	Yes	10	Yes
Accra 16	301	Public Latrine	0	19	Yes	19	Yes
Accra 16	301	Soil	1	18	Yes	17	Yes
Accra 16	302	Open Drain Water	0	48	Yes	48	Yes
Accra 16	302	Raw Produce	2	20	Yes	18	Yes
Accra 16	302	Municipal Drinking Water	0	30	Yes	30	Yes
Accra 16	302	Ocean	0	20	Yes	20	Yes
Accra 16	302	Public Latrine	1	95	Yes	94	Yes
Accra 16	302	Soil	1	40	Yes	39	Yes
Accra 16	303	Open Drain Water	0	37	Yes	37	Yes
Accra 16	303	Raw Produce	5	9	No	4	No
Accra 16	303	Municipal Drinking Water	0	9	No	9	No
Accra 16	303	Public Latrine	2	59	Yes	57	Yes
Accra 16	303	Soil	0	10	Yes	10	Yes
Accra 16	304	Open Drain Water	0	27	Yes	27	Yes
Accra 16	304	Raw Produce	1	10	Yes	9	No
Accra 16	304	Municipal Drinking Water	0	9	No	9	No
Accra 16	304	Public Latrine	1	29	Yes	28	Yes
Accra 16	304	Soil	0	10	Yes	10	Yes
Accra 16	305	Open Drain Water	0	35	Yes	35	Yes
Accra 16	305	Raw Produce	2	11	Yes	9	No
Accra 16	305	Municipal Drinking Water	0	10	Yes	10	Yes
Accra 16	305	Floodwater	0	9	No	9	No
Accra 16	305	Public Latrine	0	58	Yes	58	Yes
Accra 16	305	Soil	0	10	Yes	10	Yes
Maputo 16	801	Raw Produce	0	15	Yes	15	Yes
Maputo 16	801	Municipal Drinking Water	1	30	Yes	29	Yes
Maputo 16	801	Floodwater	0	5	No	5	No
Maputo 16	801	Public Latrine	5	30	Yes	25	Yes
Maputo 16	801	Soil	1	57	Yes	56	Yes
Maputo 16	801	Bathing Water	1	15	Yes	14	Yes
Maputo 16	802	Open Drain Water	0	7	No	7	No
Maputo 16	802	Raw Produce	1	8	No	7	No
Maputo 16	802	Municipal Drinking Water	1	10	Yes	9	No
Maputo 16	802	Floodwater	0	5	No	5	No
Maputo 16	802	Public Latrine	0	13	Yes	13	Yes
Maputo 16	802	Soil	0	19	Yes	19	Yes
Maputo 16	802	Bathing Water	0	10	Yes	10	Yes
Siem Reap 16	101	Raw Produce	2	10	Yes	8	No
Siem Reap 16	101	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	101	Soil	0	10	Yes	10	Yes
Siem Reap 16	101	Bathing Water	0	10	Yes	10	Yes
Siem Reap 16	101	Other Drinking Water	0	30	Yes	30	Yes
Siem Reap 16	102	Raw Produce	2	10	Yes	8	No
Siem Reap 16	102	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	102	Soil	0	10	Yes	10	Yes
Siem Reap 16	102	Other Drinking Water	0	30	Yes	30	Yes
Siem Reap 16	103	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	103	Soil	0	10	Yes	10	Yes
Siem Reap 16	103	Other Drinking Water	1	29	Yes	28	Yes
Siem Reap 16	104	Raw Produce	0	10	Yes	10	Yes
Siem Reap 16	104	Municipal Drinking Water	0	10	Yes	10	Yes
Siem Reap 16	104	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	104	Soil	0	10	Yes	10	Yes
Siem Reap 16	104	Other Drinking Water	0	31	Yes	31	Yes
Siem Reap 16	105	Raw Produce	0	3	No	3	No
Siem Reap 16	105	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	105	Soil	0	10	Yes	10	Yes
Siem Reap 16	105	Other Drinking Water	0	30	Yes	30	Yes
Atlanta 16	1001	Raw Produce	0	10	Yes	10	Yes
Atlanta 16	1001	Municipal Drinking Water	0	10	Yes	10	Yes
Atlanta 16	1001	Floodwater	0	7	No	7	No
Atlanta 16	1001	Public Latrine	0	10	Yes	10	Yes
Atlanta 16	1001	Soil	0	10	Yes	10	Yes
Dhaka 17	201	Open Drain Water	1	10	Yes	9	No
Dhaka 17	201	Raw Produce	1	10	Yes	9	No
Dhaka 17	201	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	201	Surface Water	0	10	Yes	10	Yes
Dhaka 17	201	Floodwater	2	10	Yes	8	No
Dhaka 17	201	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	201	Soil	2	10	Yes	8	No
Dhaka 17	201	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	201	Street Food	2	10	Yes	8	No
Dhaka 17	201	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	202	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	202	Raw Produce	2	10	Yes	8	No
Dhaka 17	202	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	202	Surface Water	0	10	Yes	10	Yes
Dhaka 17	202	Floodwater	0	10	Yes	10	Yes
Dhaka 17	202	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	202	Soil	0	10	Yes	10	Yes
Dhaka 17	202	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	202	Street Food	0	10	Yes	10	Yes
Dhaka 17	202	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	203	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	203	Raw Produce	1	10	Yes	9	No
Dhaka 17	203	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	203	Surface Water	0	10	Yes	10	Yes
Dhaka 17	203	Floodwater	0	10	Yes	10	Yes
Dhaka 17	203	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	203	Soil	1	10	Yes	9	No
Dhaka 17	203	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	203	Street Food	1	10	Yes	9	No
Dhaka 17	203	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	204	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	204	Raw Produce	2	10	Yes	8	No
Dhaka 17	204	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	204	Surface Water	1	10	Yes	9	No
Dhaka 17	204	Floodwater	2	10	Yes	8	No
Dhaka 17	204	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	204	Soil	1	10	Yes	9	No
Dhaka 17	204	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	204	Street Food	3	10	Yes	7	No
Dhaka 17	204	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	205	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	205	Raw Produce	0	10	Yes	10	Yes
Dhaka 17	205	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	205	Surface Water	0	10	Yes	10	Yes
Dhaka 17	205	Floodwater	1	10	Yes	9	No
Dhaka 17	205	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	205	Soil	0	10	Yes	10	Yes
Dhaka 17	205	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	205	Street Food	1	10	Yes	9	No
Dhaka 17	205	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	206	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	206	Raw Produce	2	10	Yes	8	No
Dhaka 17	206	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	206	Surface Water	1	10	Yes	9	No
Dhaka 17	206	Floodwater	1	10	Yes	9	No
Dhaka 17	206	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	206	Soil	1	10	Yes	9	No
Dhaka 17	206	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	206	Street Food	1	10	Yes	9	No
Dhaka 17	206	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	207	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	207	Raw Produce	2	10	Yes	8	No
Dhaka 17	207	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	207	Surface Water	0	10	Yes	10	Yes
Dhaka 17	207	Floodwater	0	10	Yes	10	Yes
Dhaka 17	207	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	207	Soil	1	10	Yes	9	No
Dhaka 17	207	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	207	Street Food	0	10	Yes	10	Yes
Dhaka 17	207	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	208	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	208	Raw Produce	1	10	Yes	9	No
Dhaka 17	208	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	208	Surface Water	0	10	Yes	10	Yes
Dhaka 17	208	Floodwater	1	10	Yes	9	No
Dhaka 17	208	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	208	Soil	2	10	Yes	8	No
Dhaka 17	208	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	208	Street Food	2	10	Yes	8	No
Dhaka 17	208	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	209	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	209	Raw Produce	2	10	Yes	8	No
Dhaka 17	209	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	209	Surface Water	2	10	Yes	8	No
Dhaka 17	209	Floodwater	0	10	Yes	10	Yes
Dhaka 17	209	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	209	Soil	2	10	Yes	8	No
Dhaka 17	209	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	209	Street Food	4	10	Yes	6	No
Dhaka 17	209	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	210	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	210	Raw Produce	0	10	Yes	10	Yes
Dhaka 17	210	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	210	Surface Water	1	10	Yes	9	No
Dhaka 17	210	Floodwater	0	10	Yes	10	Yes
Dhaka 17	210	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	210	Soil	1	10	Yes	9	No
Dhaka 17	210	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	210	Street Food	2	10	Yes	8	No
Dhaka 17	210	Other Drinking Water	0	10	Yes	10	Yes
Lusaka 18	401	Open Drain Water	2	20	Yes	18	Yes
Lusaka 18	401	Raw Produce	1	20	Yes	19	Yes
Lusaka 18	401	Municipal Drinking Water	0	10	Yes	10	Yes
Lusaka 18	401	Floodwater	10	30	Yes	20	Yes
Lusaka 18	401	Public Latrine	0	20	Yes	20	Yes
Lusaka 18	401	Soil	4	20	Yes	16	Yes
Lusaka 18	401	Street Food	1	20	Yes	19	Yes
Lusaka 18	401	Other Drinking Water	0	30	Yes	30	Yes
Accra 18	601	Open Drain Water	0	10	Yes	10	Yes
Accra 18	601	Raw Produce	0	10	Yes	10	Yes
Accra 18	601	Municipal Drinking Water	0	10	Yes	10	Yes
Accra 18	601	Floodwater	0	4	No	4	No
Accra 18	601	Public Latrine	0	10	Yes	10	Yes
Accra 18	601	Soil	0	10	Yes	10	Yes
Accra 18	601	Bathing Water	0	4	No	4	No
Accra 18	601	Street Food	0	10	Yes	10	Yes
Accra 18	602	Open Drain Water	0	11	Yes	11	Yes
Accra 18	602	Raw Produce	0	10	Yes	10	Yes
Accra 18	602	Municipal Drinking Water	0	10	Yes	10	Yes
Accra 18	602	Ocean	0	10	Yes	10	Yes
Accra 18	602	Floodwater	0	5	No	5	No
Accra 18	602	Public Latrine	0	10	Yes	10	Yes
Accra 18	602	Soil	0	10	Yes	10	Yes
Accra 18	602	Bathing Water	0	5	No	5	No
Accra 18	602	Street Food	0	10	Yes	10	Yes
Kumasi 18	701	Open Drain Water	0	10	Yes	10	Yes
Kumasi 18	701	Raw Produce	1	9	No	8	No
Kumasi 18	701	Municipal Drinking Water	0	10	Yes	10	Yes
Kumasi 18	701	Floodwater	0	6	No	6	No
Kumasi 18	701	Public Latrine	0	10	Yes	10	Yes
Kumasi 18	701	Soil	0	10	Yes	10	Yes
Kumasi 18	701	Bathing Water	0	3	No	3	No
Kumasi 18	701	Street Food	1	10	Yes	9	No
Kumasi 18	702	Open Drain Water	0	10	Yes	10	Yes
Kumasi 18	702	Raw Produce	0	10	Yes	10	Yes
Kumasi 18	702	Municipal Drinking Water	0	10	Yes	10	Yes
Kumasi 18	702	Floodwater	0	10	Yes	10	Yes
Kumasi 18	702	Public Latrine	1	9	No	8	No
Kumasi 18	702	Soil	0	10	Yes	10	Yes
Kumasi 18	702	Bathing Water	0	10	Yes	10	Yes
Kumasi 18	702	Street Food	0	10	Yes	10	Yes
Kumasi 18	703	Open Drain Water	0	10	Yes	10	Yes
Kumasi 18	703	Raw Produce	0	10	Yes	10	Yes
Kumasi 18	703	Municipal Drinking Water	0	10	Yes	10	Yes
Kumasi 18	703	Surface Water	0	3	No	3	No
Kumasi 18	703	Floodwater	0	10	Yes	10	Yes
Kumasi 18	703	Public Latrine	0	4	No	4	No
Kumasi 18	703	Soil	0	10	Yes	10	Yes
Kumasi 18	703	Bathing Water	0	8	No	8	No
Kumasi 18	703	Street Food	0	10	Yes	10	Yes
Kumasi 18	704	Open Drain Water	0	10	Yes	10	Yes
Kumasi 18	704	Raw Produce	0	10	Yes	10	Yes
Kumasi 18	704	Municipal Drinking Water	0	6	No	6	No
Kumasi 18	704	Floodwater	0	10	Yes	10	Yes
Kumasi 18	704	Public Latrine	0	4	No	4	No
Kumasi 18	704	Soil	0	10	Yes	10	Yes
Kumasi 18	704	Street Food	0	10	Yes	10	Yes
Kampala 18	901	Open Drain Water	0	9	No	9	No
Kampala 18	901	Raw Produce	1	10	Yes	9	No
Kampala 18	901	Surface Water	3	8	No	5	No
Kampala 18	901	Floodwater	0	10	Yes	10	Yes
Kampala 18	901	Public Latrine	3	10	Yes	7	No
Kampala 18	901	Soil	2	10	Yes	8	No
Kampala 18	901	Street Food	5	10	Yes	5	No
Kampala 18	902	Open Drain Water	0	9	No	9	No
Kampala 18	902	Raw Produce	4	10	Yes	6	No
Kampala 18	902	Municipal Drinking Water	0	10	Yes	10	Yes
Kampala 18	902	Floodwater	0	10	Yes	10	Yes
Kampala 18	902	Public Latrine	0	10	Yes	10	Yes
Kampala 18	902	Soil	1	10	Yes	9	No
Kampala 18	902	Street Food	3	9	No	6	No
Kampala 18	902	Other Drinking Water	1	10	Yes	9	No
Kampala 18	903	Open Drain Water	0	10	Yes	10	Yes
Kampala 18	903	Raw Produce	1	10	Yes	9	No
Kampala 18	903	Municipal Drinking Water	0	9	No	9	No
Kampala 18	903	Surface Water	0	4	No	4	No
Kampala 18	903	Floodwater	0	10	Yes	10	Yes
Kampala 18	903	Public Latrine	0	10	Yes	10	Yes
Kampala 18	903	Soil	1	10	Yes	9	No
Kampala 18	903	Street Food	0	6	No	6	No
Kampala 18	903	Other Drinking Water	0	9	No	9	No
Kampala 18	904	Open Drain Water	0	7	No	7	No
Kampala 18	904	Raw Produce	3	10	Yes	7	No
Kampala 18	904	Municipal Drinking Water	0	10	Yes	10	Yes
Kampala 18	904	Floodwater	3	10	Yes	7	No
Kampala 18	904	Public Latrine	0	10	Yes	10	Yes
Kampala 18	904	Soil	4	10	Yes	6	No
Kampala 18	904	Street Food	4	10	Yes	6	No
Kampala 18	904	Other Drinking Water	1	10	Yes	9	No
Kampala 18	905	Open Drain Water	0	12	Yes	12	Yes
Kampala 18	905	Raw Produce	0	10	Yes	10	Yes
Kampala 18	905	Municipal Drinking Water	0	10	Yes	10	Yes
Kampala 18	905	Floodwater	1	10	Yes	9	No
Kampala 18	905	Public Latrine	0	10	Yes	10	Yes
Kampala 18	905	Soil	2	10	Yes	8	No
Kampala 18	905	Street Food	0	10	Yes	10	Yes
Kampala 18	905	Other Drinking Water	3	10	Yes	7	No
Lusaka 19	1301	Open Drain Water	0	10	Yes	10	Yes
Lusaka 19	1301	Raw Produce	0	10	Yes	10	Yes
Lusaka 19	1301	Municipal Drinking Water	0	10	Yes	10	Yes
Lusaka 19	1301	Surface Water	0	10	Yes	10	Yes
Lusaka 19	1301	Public Latrine	0	10	Yes	10	Yes
Lusaka 19	1301	Soil	2	10	Yes	8	No
Lusaka 19	1301	Street Food	0	10	Yes	10	Yes
Lusaka 19	1301	Other Drinking Water	0	20	Yes	20	Yes
Lusaka 19	1302	Open Drain Water	0	10	Yes	10	Yes
Lusaka 19	1302	Raw Produce	0	10	Yes	10	Yes
Lusaka 19	1302	Municipal Drinking Water	0	10	Yes	10	Yes
Lusaka 19	1302	Public Latrine	0	10	Yes	10	Yes
Lusaka 19	1302	Soil	0	10	Yes	10	Yes
Lusaka 19	1302	Street Food	0	10	Yes	10	Yes
Lusaka 19	1302	Other Drinking Water	0	20	Yes	20	Yes
Lusaka 19	1303	Open Drain Water	0	10	Yes	10	Yes
Lusaka 19	1303	Raw Produce	0	10	Yes	10	Yes
Lusaka 19	1303	Municipal Drinking Water	0	10	Yes	10	Yes
Lusaka 19	1303	Public Latrine	0	10	Yes	10	Yes
Lusaka 19	1303	Soil	0	10	Yes	10	Yes
Lusaka 19	1303	Street Food	0	10	Yes	10	Yes
Lusaka 19	1303	Other Drinking Water	0	20	Yes	20	Yes
Dakar 19	1201	Open Drain Water	0	10	Yes	10	Yes
Dakar 19	1201	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1201	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1201	Soil	2	10	Yes	8	No
Dakar 19	1201	Street Food	1	10	Yes	9	No
Dakar 19	1202	Open Drain Water	1	10	Yes	9	No
Dakar 19	1202	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1202	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1202	Soil	0	10	Yes	10	Yes
Dakar 19	1202	Street Food	0	10	Yes	10	Yes
Dakar 19	1203	Open Drain Water	1	10	Yes	9	No
Dakar 19	1203	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1203	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1203	Soil	1	10	Yes	9	No
Dakar 19	1203	Street Food	0	10	Yes	10	Yes
Dakar 19	1204	Open Drain Water	7	10	Yes	3	No
Dakar 19	1204	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1204	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1204	Soil	1	10	Yes	9	No
Dakar 19	1204	Street Food	0	10	Yes	10	Yes
Dakar 19	1205	Open Drain Water	0	10	Yes	10	Yes
Dakar 19	1205	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1205	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1205	Soil	1	10	Yes	9	No
Dakar 19	1205	Street Food	0	10	Yes	10	Yes

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Sample Types

E. coli count per Sample Types

sample_type_name	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Open Drain Water	493	-0.30	9.58	7.69	8.39	16.77
Raw Produce	465	1.40	7.00	5.29	6.04	12.08
Municipal Drinking Water	485	-0.30	4.76	2.70	3.55	7.10
Ocean	40	1.95	6.30	5.59	5.79	11.59
Surface Water	125	1.30	7.38	5.99	6.49	12.97
Floodwater	311	0.70	8.08	6.20	6.88	13.77
Public Latrine	612	-0.15	4.45	3.03	3.66	7.31
Soil	628	-1.00	5.60	3.97	4.68	9.35
Bathing Water	210	-0.30	4.38	2.85	3.48	6.97
Street Food	305	0.69	6.55	4.82	5.52	11.04
Other Drinking Water	379	-0.30	3.86	2.37	2.90	5.80

Samples - Membrane Filtration

E. coli count per Sample Types - Membrane Filtration

sample_type_name	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Open Drain Water	343	-0.30	9.58	7.77	8.46	16.92
Raw Produce	305	1.40	7.00	5.44	6.13	12.25
Municipal Drinking Water	335	-0.30	3.30	1.47	2.28	4.55
Ocean	40	1.95	6.30	5.59	5.79	11.59
Surface Water	15	1.48	4.94	4.00	4.39	8.79
Floodwater	174	0.70	7.27	5.91	6.33	12.67
Public Latrine	452	-0.15	4.45	3.16	3.72	7.44
Soil	468	-1.00	5.60	4.05	4.73	9.46
Bathing Water	110	-0.30	3.88	2.44	2.93	5.87
Street Food	155	1.32	6.55	4.73	5.56	11.11
Other Drinking Water	189	-0.30	3.40	2.16	2.62	5.24

Samples - IDEXX

E. coli count per Sample Types - IDEXX

sample_type_name	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Open Drain Water	150	2.00	8.71	7.40	7.78	15.56
Raw Produce	160	1.40	6.06	4.64	5.22	10.44
Municipal Drinking Water	150	-0.30	4.76	3.18	3.79	7.59
Surface Water	110	1.30	7.38	6.04	6.51	13.02
Floodwater	137	1.70	8.08	6.43	7.06	14.12
Public Latrine	160	-0.15	2.80	1.13	1.77	3.55
Soil	160	0.00	5.09	3.57	4.21	8.43
Bathing Water	100	-0.30	4.38	3.06	3.63	7.26
Street Food	150	0.69	6.44	4.91	5.47	10.94
Other Drinking Water	190	-0.30	3.86	2.51	3.01	6.03

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Produce

Contamination of Produce

Differences within Produce Samples

General comparison tables indicating how many samples were taken overall.

Types of Produce and Number of Samples

id	produce	n
1	cabbage	33
2	cucumber	73
3	egg plant	2
4	lettuce	34
5	long bean	2
6	long plant	2
7	salad	14
8	tomato	162
9	water mimosa	1
10	wing bean	2
11	apple	6
12	carrot	11
13	pepper	66
14	spring onion	3
15	guava	4
16	coriander	35
17	green chilly	10
18	okra	3
19	watermelon	1

Types of Produce, by Deployment

citylabel	n	produce
Vellore 14	8	tomato
Vellore 14	4	coriander
Vellore 14	5	green chilly
Vellore 14	3	okra
Accra 16	4	cabbage
Accra 16	1	cucumber
Accra 16	2	lettuce
Accra 16	22	tomato
Accra 16	1	apple
Accra 16	3	carrot
Accra 16	34	pepper
Accra 16	3	spring onion
Maputo 16	7	cucumber
Maputo 16	8	lettuce
Maputo 16	8	tomato
Siem Reap 16	4	cabbage
Siem Reap 16	8	cucumber
Siem Reap 16	2	egg plant
Siem Reap 16	5	lettuce
Siem Reap 16	2	long bean
Siem Reap 16	2	long plant
Siem Reap 16	4	salad
Siem Reap 16	3	tomato
Siem Reap 16	1	water mimosa
Siem Reap 16	2	wing bean
Atlanta 16	1	cucumber
Atlanta 16	2	lettuce
Atlanta 16	3	tomato
Atlanta 16	3	pepper
Dhaka 17	35	cucumber
Dhaka 17	34	tomato
Dhaka 17	31	coriander
Lusaka 18	11	cucumber
Lusaka 18	5	apple
Lusaka 18	4	guava
Accra 18	4	lettuce
Accra 18	9	tomato
Accra 18	7	pepper
Kumasi 18	13	lettuce
Kumasi 18	13	tomato
Kumasi 18	13	pepper
Kampala 18	25	cabbage
Kampala 18	25	tomato
Lusaka 19	1	cucumber
Lusaka 19	28	tomato
Lusaka 19	1	watermelon
Dakar 19	9	cucumber
Dakar 19	10	salad
Dakar 19	9	tomato
Dakar 19	8	carrot
Dakar 19	9	pepper
Dakar 19	5	green chilly

E. coli count per Types of Produce

produce	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
apple	6	1.40	2.41	1.81	1.98	3.96
cabbage	33	1.40	5.73	4.68	5.05	10.09
carrot	11	2.40	5.22	4.48	4.71	9.41
coriander	35	2.70	6.06	5.28	5.51	11.02
cucumber	73	1.40	7.00	5.69	6.32	12.64
egg plant	2	4.11	4.83	4.61	4.59	9.19
green chilly	10	2.40	6.00	5.34	5.61	11.21
guava	4	1.40	5.50	5.02	5.26	10.52
lettuce	34	1.40	7.00	5.65	6.24	12.48
long bean	2	2.00	2.48	2.30	2.15	4.30
long plant	2	3.22	3.48	3.37	2.98	5.96
okra	3	3.57	4.10	3.83	3.70	7.39
pepper	66	1.40	6.00	5.18	5.50	11.00
salad	14	2.40	5.82	5.20	5.29	10.58
spring onion	3	2.60	4.60	4.31	4.45	8.89
tomato	162	1.40	7.00	4.99	5.91	11.83
water mimosa	1	4.80	4.80	4.80
watermelon	1	1.40	1.40	1.40
wing bean	2	2.81	4.81	4.51	4.65	9.30
	1	1.40	1.40	1.40

Graphs - Differences within Produce Samples

E. coli count by deployment and neighborhood and type of produce

E. coli count by deployment and neighborhood and type of produce

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Latrines

Latrines Types from Sample Collection

Types of Latrines in all deployments

n	Type
45	Flush/pour flush to pit
310	Flush/pour flush to septic tank
66	Flush/pour flush to sewage system
70	Pit latrine with slab
75	VIP
4	Open pit latrine (without slab)
8	Other

## Warning: Column `col_UID`/`UID` joining factor and character vector,
## coercing into character vector

Types of Latrines by deployment

citylabel	n	Type
Maputo 15	4	Flush/pour flush to pit
Maputo 15	24	Pit latrine with slab
Accra 16	240	Flush/pour flush to septic tank
Accra 16	20	VIP
Maputo 16	19	Flush/pour flush to pit
Maputo 16	5	Pit latrine with slab
Maputo 16	17	VIP
Maputo 16	2	Other
Dhaka 17	6	Flush/pour flush to septic tank
Dhaka 17	64	Flush/pour flush to sewage system
Dhaka 17	10	Pit latrine with slab
Dhaka 17	12	VIP
Dhaka 17	4	Open pit latrine (without slab)
Dhaka 17	4	Other
Lusaka 18	2	Flush/pour flush to pit
Lusaka 18	3	Flush/pour flush to septic tank
Lusaka 18	1	Flush/pour flush to sewage system
Lusaka 18	11	Pit latrine with slab
Lusaka 18	3	VIP
Accra 18	19	Flush/pour flush to septic tank
Accra 18	1	Other
Kumasi 18	20	Flush/pour flush to septic tank
Kumasi 18	7	Pit latrine with slab
Kampala 18	4	Flush/pour flush to pit
Kampala 18	8	Flush/pour flush to septic tank
Kampala 18	1	Flush/pour flush to sewage system
Kampala 18	13	Pit latrine with slab
Kampala 18	23	VIP
Kampala 18	1	Other
Lusaka 19	16	Flush/pour flush to pit
Lusaka 19	14	Flush/pour flush to septic tank

Overall E. coli count per Types of Latrines

Type	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Flush/pour flush to pit	45	-0.15	4.45	2.89	3.65	7.29
Flush/pour flush to septic tank	310	-0.15	4.45	3.21	3.76	7.52
Flush/pour flush to sewage system	66	-0.15	3.05	1.58	2.20	4.41
Pit latrine with slab	70	-0.15	3.45	2.30	2.80	5.61
VIP	75	-0.15	4.45	2.98	3.58	7.15
Open pit latrine (without slab)	4	-0.15	1.85	1.26	1.54	3.09
Other	8	-0.15	2.75	1.86	2.29	4.59
	34	-0.15	3.45	2.89	3.06	6.12

E. coli count per Types of Latrines by Deployment

Type	citylabel	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
	Vellore 14	24	-0.15	3.45	3.04	3.09	6.18
Flush/pour flush to pit	Maputo 15	4	-0.15	1.32	0.76	1.01	2.01
Pit latrine with slab	Maputo 15	24	-0.15	3.45	2.56	2.97	5.95
Flush/pour flush to septic tank	Accra 16	240	-0.15	4.45	3.31	3.81	7.62
VIP	Accra 16	20	-0.15	4.45	3.52	3.83	7.66
Flush/pour flush to pit	Maputo 16	19	-0.15	4.45	3.31	3.86	7.71
Pit latrine with slab	Maputo 16	5	-0.15	0.62	0.23	0.16	0.32
VIP	Maputo 16	17	-0.15	1.84	0.93	1.28	2.56
Other	Maputo 16	2	0.45	2.75	2.45	2.60	5.19
	Atlanta 16	10	-0.15	-0.15	-0.15	-Inf	-Inf
Flush/pour flush to septic tank	Dhaka 17	6	-0.15	1.88	1.13	1.49	2.98
Flush/pour flush to sewage system	Dhaka 17	64	-0.15	2.80	1.33	1.94	3.88
Pit latrine with slab	Dhaka 17	10	-0.15	2.10	1.45	1.69	3.37
VIP	Dhaka 17	12	-0.15	1.19	0.30	0.63	1.25
Open pit latrine (without slab)	Dhaka 17	4	-0.15	1.85	1.26	1.54	3.09
Other	Dhaka 17	4	-0.15	-0.15	-0.15	-Inf	-Inf
Flush/pour flush to pit	Lusaka 18	2	-0.15	-0.15	-0.15	-Inf	-Inf
Flush/pour flush to septic tank	Lusaka 18	3	-0.15	-0.15	-0.15	-Inf	-Inf
Flush/pour flush to sewage system	Lusaka 18	1	-0.15	-0.15	-0.15
Pit latrine with slab	Lusaka 18	11	-0.15	0.15	-0.08	-0.55	-1.10
VIP	Lusaka 18	3	-0.15	-0.15	-0.15	-Inf	-Inf
Flush/pour flush to septic tank	Accra 18	19	-0.15	2.62	1.44	1.98	3.96
Other	Accra 18	1	-0.15	-0.15	-0.15
Flush/pour flush to septic tank	Kumasi 18	20	-0.15	4.05	2.90	3.43	6.85
Pit latrine with slab	Kumasi 18	7	-0.15	3.10	2.27	2.68	5.35
Flush/pour flush to pit	Kampala 18	4	-0.15	2.75	2.30	2.49	4.99
Flush/pour flush to septic tank	Kampala 18	8	-0.15	2.92	2.08	2.47	4.93
Flush/pour flush to sewage system	Kampala 18	1	3.05	3.05	3.05
Pit latrine with slab	Kampala 18	13	-0.15	3.23	2.43	2.78	5.57
VIP	Kampala 18	23	-0.15	3.53	2.23	2.85	5.71
Other	Kampala 18	1	1.29	1.29	1.29
Flush/pour flush to pit	Lusaka 19	16	-0.15	1.30	0.37	0.68	1.35
Flush/pour flush to septic tank	Lusaka 19	14	-0.15	2.28	1.29	1.70	3.41

E. coli count of all latrines by Neighborhood

citylabel	Neighborhood	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Vellore 14	1101	12	0.15	3.45	3.07	3.10	6.20
Vellore 14	1102	12	-0.15	3.45	3.02	3.10	6.19
Maputo 15	501	15	-0.15	3.45	2.27	2.86	5.72
Maputo 15	502	13	-0.15	3.45	2.67	3.02	6.03
Accra 16	301	19	-0.15	4.45	3.72	3.94	7.88
Accra 16	302	95	-0.15	4.02	2.59	3.22	6.45
Accra 16	303	59	-0.15	4.45	3.56	3.94	7.88
Accra 16	304	29	-0.15	4.45	3.34	3.87	7.73
Accra 16	305	58	-0.15	4.45	3.37	3.84	7.69
Maputo 16	801	30	-0.15	3.23	2.04	2.55	5.10
Maputo 16	802	13	-0.15	4.45	3.34	3.89	7.78
Atlanta 16	1001	10	-0.15	-0.15	-0.15	-Inf	-Inf
Dhaka 17	201	10	-0.15	1.88	0.92	1.38	2.76
Dhaka 17	202	10	-0.15	1.55	0.80	1.06	2.13
Dhaka 17	203	10	-0.15	1.85	0.89	1.34	2.69
Dhaka 17	204	10	-0.15	2.42	1.50	1.92	3.83
Dhaka 17	205	10	-0.15	-0.15	-0.15	-Inf	-Inf
Dhaka 17	206	10	-0.15	2.80	1.89	2.29	4.58
Dhaka 17	207	10	-0.15	0.76	0.08	0.20	0.40
Dhaka 17	208	10	-0.15	2.10	1.48	1.68	3.36
Dhaka 17	209	10	-0.15	2.25	1.27	1.75	3.50
Dhaka 17	210	10	-0.15	0.76	0.11	0.20	0.40
Lusaka 18	401	20	-0.15	0.15	-0.11	-0.67	-1.33
Accra 18	601	10	-0.15	1.45	0.55	0.93	1.87
Accra 18	602	10	-0.15	2.62	1.69	2.12	4.23
Kumasi 18	701	10	0.75	3.10	2.16	2.59	5.19
Kumasi 18	702	9	-0.15	4.05	3.27	3.60	7.21
Kumasi 18	703	4	-0.15	1.32	0.97	0.95	1.90
Kumasi 18	704	4	-0.15	0.62	0.32	0.23	0.47
Kampala 18	901	10	-0.15	1.83	1.22	1.43	2.86
Kampala 18	902	10	-0.15	3.23	2.28	2.73	5.46
Kampala 18	903	10	0.85	3.15	2.61	2.73	5.46
Kampala 18	904	10	-0.15	-0.15	-0.15	-Inf	-Inf
Kampala 18	905	10	-0.15	3.53	2.55	3.02	6.05
Lusaka 19	1301	10	-0.15	2.28	1.40	1.77	3.55
Lusaka 19	1302	10	-0.15	1.45	0.59	0.93	1.87
Lusaka 19	1303	10	-0.15	1.02	0.31	0.50	1.01

Graphs - Differences within Latrine Types

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Streetfood

Differences within Streetfood

Streetfood is standardized as one serving size of food available from street vendors.

## Warning: Column `col_UID`/`UID` joining factor and character vector,
## coercing into character vector

Streetfood E. coli count per deployment

citylabel	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Dhaka 17	100	0.69	6.44	5.11	5.56	11.12
Lusaka 18	20	0.70	1.99	1.59	1.53	3.06
Accra 18	20	1.95	6.55	5.52	5.96	11.93
Kumasi 18	40	1.32	4.34	3.12	3.66	7.33
Kampala 18	45	1.54	5.56	4.37	4.86	9.72
Lusaka 19	30	0.98	1.99	1.55	1.26	2.51
Dakar 19	50	2.67	3.83	2.88	3.01	6.01

Streetfood E. coli count per neighborhood

neighb_UID	citylabel	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
201	Dhaka 17	10	0.69	5.50	4.81	5.08	10.16
202	Dhaka 17	10	2.32	5.83	5.14	5.34	10.68
203	Dhaka 17	10	2.01	6.44	5.67	5.99	11.97
204	Dhaka 17	10	1.67	5.94	5.12	5.52	11.03
205	Dhaka 17	10	1.22	4.48	3.88	4.04	8.08
206	Dhaka 17	10	2.52	5.62	4.97	5.22	10.45
207	Dhaka 17	10	2.04	4.45	3.76	3.98	7.96
208	Dhaka 17	10	3.53	5.79	5.04	5.32	10.64
209	Dhaka 17	10	1.80	5.27	4.80	4.93	9.85
210	Dhaka 17	10	2.27	5.80	5.26	5.37	10.74
401	Lusaka 18	20	0.70	1.99	1.59	1.53	3.06
601	Accra 18	10	2.00	6.55	5.64	6.05	12.09
602	Accra 18	10	1.95	6.37	5.37	5.87	11.73
701	Kumasi 18	10	1.32	3.16	2.63	2.76	5.51
702	Kumasi 18	10	1.84	2.30	2.06	1.66	3.33
703	Kumasi 18	10	1.79	4.34	3.65	3.94	7.87
704	Kumasi 18	10	1.83	2.97	2.29	2.42	4.84
901	Kampala 18	10	1.54	5.56	4.92	5.20	10.40
902	Kampala 18	9	1.91	5.31	4.75	4.90	9.80
903	Kampala 18	6	1.90	2.26	2.09	1.67	3.34
904	Kampala 18	10	1.71	3.85	3.34	3.42	6.85
905	Kampala 18	10	1.70	3.49	2.61	2.98	5.95
1201	Dakar 19	10	2.67	3.00	2.76	2.24	4.47
1202	Dakar 19	10	2.67	2.83	2.74	1.95	3.89
1203	Dakar 19	10	2.67	2.83	2.74	1.94	3.89
1204	Dakar 19	10	2.67	3.61	2.98	3.04	6.08
1205	Dakar 19	10	2.67	3.83	3.07	3.30	6.59
1301	Lusaka 19	10	1.12	1.99	1.61	1.40	2.80
1302	Lusaka 19	10	1.45	1.84	1.59	1.04	2.09
1303	Lusaka 19	10	0.98	1.74	1.44	1.14	2.29

Graphs - Differences within Produce Samples

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Drinking Water

Differences within Drinking Water

Municipal Drinking Water E. coli count per deployment

citylabel	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Vellore 14	22	-0.3	2.30	1.23	1.71	3.42
Accra 16	68	-0.3	3.30	1.96	2.55	5.09
Maputo 16	40	-0.3	2.77	1.46	1.99	3.98
Siem Reap 16	10	-0.3	-0.30	-0.30	-Inf	-Inf
Atlanta 16	10	-0.3	-0.30	-0.30	-Inf	-Inf
Dhaka 17	100	-0.3	4.76	3.36	3.88	7.75
Lusaka 18	10	-0.3	0.30	-0.19	-0.32	-0.65
Accra 18	20	-0.3	1.91	0.93	1.30	2.59
Kumasi 18	36	-0.3	3.22	1.71	2.44	4.88
Kampala 18	39	-0.3	1.11	-0.01	0.35	0.70
Lusaka 19	30	-0.3	2.27	1.17	1.65	3.29
Dakar 19	100	-0.3	0.00	-0.30	-1.30	-2.60

Municipal Drinking Water E. coli count per neighborhood

neighb_UID	citylabel	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
104	Siem Reap 16	10	-0.30	-0.30	-0.30	-Inf	-Inf
201	Dhaka 17	10	-0.30	1.38	0.70	0.90	1.79
202	Dhaka 17	10	-0.30	1.71	0.90	1.22	2.44
203	Dhaka 17	10	-0.30	1.35	0.64	0.86	1.71
204	Dhaka 17	10	-0.30	4.38	3.48	3.88	7.75
205	Dhaka 17	10	-0.30	2.18	1.19	1.68	3.36
206	Dhaka 17	10	-0.30	4.30	3.32	3.80	7.59
207	Dhaka 17	10	0.78	4.38	3.86	3.99	7.98
208	Dhaka 17	10	1.56	4.76	4.00	4.24	8.48
209	Dhaka 17	10	-0.30	2.62	2.18	2.18	4.36
210	Dhaka 17	10	-0.30	3.41	2.45	2.91	5.81
301	Accra 16	10	-0.30	1.38	0.95	1.01	2.03
302	Accra 16	30	-0.30	3.30	2.30	2.71	5.42
303	Accra 16	9	-0.30	0.00	-0.21	-0.66	-1.31
304	Accra 16	9	-0.30	0.48	-0.11	-0.08	-0.16
305	Accra 16	10	-0.30	-0.30	-0.30	-Inf	-Inf
401	Lusaka 18	10	-0.30	0.30	-0.19	-0.32	-0.65
601	Accra 18	10	-0.30	1.91	1.00	1.40	2.81
602	Accra 18	10	-0.30	1.63	0.84	1.12	2.24
701	Kumasi 18	10	-0.30	1.49	1.08	1.02	2.03
702	Kumasi 18	10	-0.30	3.22	2.23	2.72	5.44
703	Kumasi 18	10	-0.30	0.30	-0.05	-0.34	-0.68
704	Kumasi 18	6	-0.30	1.40	0.66	1.00	2.00
801	Maputo 16	30	-0.30	2.77	1.53	2.05	4.09
802	Maputo 16	10	-0.30	1.72	1.10	1.26	2.52
902	Kampala 18	10	-0.30	-0.30	-0.30	-Inf	-Inf
903	Kampala 18	9	-0.30	-0.30	-0.30	-Inf	-Inf
904	Kampala 18	10	-0.30	-0.30	-0.30	-Inf	-Inf
905	Kampala 18	10	-0.30	1.11	0.38	0.63	1.26
1001	Atlanta 16	10	-0.30	-0.30	-0.30	-Inf	-Inf
1101	Vellore 14	11	-0.30	2.00	1.06	1.52	3.04
1102	Vellore 14	11	-0.30	2.30	1.36	1.82	3.65
1201	Dakar 19	20	-0.30	-0.30	-0.30	-Inf	-Inf
1202	Dakar 19	20	-0.30	0.00	-0.28	-0.95	-1.90
1203	Dakar 19	20	-0.30	-0.30	-0.30	-Inf	-Inf
1204	Dakar 19	20	-0.30	-0.30	-0.30	-Inf	-Inf
1205	Dakar 19	20	-0.30	-0.30	-0.30	-Inf	-Inf
1301	Lusaka 19	10	-0.30	-0.30	-0.30	-Inf	-Inf
1302	Lusaka 19	10	-0.30	0.00	-0.26	-0.80	-1.60
1303	Lusaka 19	10	-0.30	2.27	1.64	1.85	3.69

Graphs - Differences within Drinking Water Samples

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#need to write separate function for this kind of Qs –>

Behavior - Comb

Adults - Combined Frequency

Yes/ No Questions

Children - Combined Frequency

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Exposure

Adults

The SaniPath exposure data is displayed here by pathway. The different colors represent the individual deployment, while a dot represents a particular neighborhood.

Children

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Map

Country Map

Neighborhood Map

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The end.